Numbers, predictions, and activations
Learning goal
Build small functions and explain what their outputs mean.
Start with an average, a maximum value, and a threshold classifier. max returns the largest value; argmax returns its position. For single-label classification, that position can identify the highest-scoring class. A score is not automatically a calibrated probability. Changing a decision threshold can change which examples are classified as positive.
A closer look
A neuron can combine inputs as z = w·x + b and apply an activation function. Sigmoid maps a finite real number to a value between 0 and 1; it is one activation, not the definition of every neuron. Other networks use ReLU or GELU. Implementing sigmoid shows a building block, not a trained network. The missions then introduce models fitted from data; passing examples is a checkpoint, not proof of generalization.
Explain it yourself
For [0.2, 0.7, 0.1], what do max and argmax return using zero-based indexing?
Compare with an explanation
max = 0.7; argmax = 1. A value and its index are different outputs.
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